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超越传统的刺激:验证人工智能生成的图像在影响研究中引起负面情绪
Hey Tou Chiu1,2, Hoi In Sou1,2, Yuen Wing Lam1,2
1Department of Educational Psychology, The Chinese University of Hong Kong, Hong Kong.
PloS one
|February 10, 2026
概括
生成型人工智能可以为研究创建定制的负面和中性情感图像. 这些人工智能生成的刺激显示了与传统图像相似的价值和兴奋模式,提供了灵活的替代方案.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 神经科学是一个神经科学.
背景情况:
- 情感研究依赖于标准化的图像集来进行情感反应.
- 现有的数据库可能缺乏临床研究所需的特定负面刺激.
- 生成型人工智能为创建定制情感刺激提供了一个潜在的解决方案.
研究的目的:
- 评估生成性AI (文本到图像生成器) 对于创建负面和中性情感刺激.
- 将人工智能生成的图像的情感特性与既定规范数据进行比较.
- 评估人工智能生成图像作为传统刺激的替代品的可行性.
主要方法:
- 使用人工智能生成的图像进行了两项研究.
- 参与者对160张和200张人工智能生成的图像的价值和兴奋程度进行了评分.
- 情感评分与标准化图像数据库中的评分进行了比较.
主要成果:
- 人工智能生成的负和中性图像表明了价值和兴奋之间预期的反向关系.
- 在价值和兴奋维度之间发现了中度至强度的相关性.
- 人工智能刺激成功地复制了传统情感数据库中观察到的模式.
结论:
- 生成型人工智能是一种可行的工具,可以创建定制的情感刺激.
- 人工智能生成的图像可以作为具体研究需求的实际替代方案.
- 这种方法支持为情绪研究和临床应用量身定制的刺激创造.
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